This assignment is for ETC5521 Assignment 1 by Team EMU comprising of Min Min Soh and Rohan Baghel.

1 Introduction and motivation

Global fishing receives a great deal of attention in the media for the past decades. The rise of world population has increased the demand for seafood across the world. Meanwhile, the health of fish population in the world remains as a concern with the rise of global fishing. Fish farming (or ‘aquaculture’)helps to contribute to the seafood production while alleviating the pressure of wild fisheries.

This study is based on the data sets of global fishing. We explore these data by analyzing the performance for each country/region in terms of consumption, production and fish stock level. Moreover, we compare different data sets to reveal unexpected findings.

We begin by describing the data in the next section, including the way we source it and prepare it for analysis.

In the analysis section, we present our observations through graphical displays. Our main tool is R, a programming language for statistical computing and graphics.

2 Data description

The data set has been obtained from tidytuesday r package or through the website https://ourworldindata.org. The data comprises of four files in the “.csv” format which is machine readable and can be used to analyze the state of fish production and consumption in the world. The data dictionary for the data set has been given below. They define the variables and their types in each of the data sets.

2.1 capture-fisheries-vs-aquaculture.csv

2.1.1 ‘captured_vs_farmed’ - Data Dictionary

Entity: Country/entity Code: Country code Year: Year Aquaculture production (metric tons): Production of aquaculture Capture fisheries production (metric tons): Captured aquaculture ——————————————|:———|:———–|

## spec_tbl_df [14,674 × 5] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ Entity                                    : chr [1:14674] "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
##  $ Code                                      : chr [1:14674] "AFG" "AFG" "AFG" "AFG" ...
##  $ Year                                      : num [1:14674] 1969 1970 1971 1972 1973 ...
##  $ Aquaculture production (metric tons)      : num [1:14674] 60 60 60 60 60 60 60 170 170 170 ...
##  $ Capture fisheries production (metric tons): num [1:14674] 400 400 500 500 500 500 600 600 600 600 ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   Entity = col_character(),
##   ..   Code = col_character(),
##   ..   Year = col_double(),
##   ..   `Aquaculture production (metric tons)` = col_double(),
##   ..   `Capture fisheries production (metric tons)` = col_double()
##   .. )
##  - attr(*, "problems")=<externalptr>

The data has 5 variables and 14,674 observations. The ‘Code’ variable is character while others are numeric .

2.1.2 ‘captured_vs_farmed’ - Data quality Check

The data has 8.1% missing value. Most of them are in the variables of ‘aquaculture’ and ‘code’.

2.2 fish-and-seafood-consumption-per-capita.csv

2.2.1 ‘consumption’ - Data Dictionary

Entity: Country/entity Code: Country code Year: Year Fish,Seafood-Food supply quantity(kg/capita/yr)(FAO, 2020):Food supply in fish

## spec_tbl_df [11,028 × 4] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ Entity                                                        : chr [1:11028] "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
##  $ Code                                                          : chr [1:11028] "AFG" "AFG" "AFG" "AFG" ...
##  $ Year                                                          : num [1:11028] 1961 1962 1963 1964 1965 ...
##  $ Fish, Seafood- Food supply quantity (kg/capita/yr) (FAO, 2020): num [1:11028] 0.03 0.03 0.03 0.03 0.03 0.03 0.04 0.04 0.04 0.04 ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   Entity = col_character(),
##   ..   Code = col_character(),
##   ..   Year = col_double(),
##   ..   `Fish, Seafood- Food supply quantity (kg/capita/yr) (FAO, 2020)` = col_double()
##   .. )
##  - attr(*, "problems")=<externalptr>

As we can see, there are 4 variables and 11,028 observation in total. The ‘Entityy’ and ‘Code’ variable are character while others are numerical.

2.2.2 ‘consumption’ - Data quality Check

15.72% of values in ‘Code’ are missing. This does not affect our analysis.

2.3 fish-stocks-within-sustainable-levels.csv

2.3.1 ‘stock’ - Data Dictionary

Entity: Country/entity Code: code Year: Year Share of fish stocks within biologically sustainable levels: Share of sustainable fish stock Share of fish stocks that are overexploited: Share of fish stock that are overexploited

## spec_tbl_df [51 × 5] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ Entity                                                                 : chr [1:51] "Eastern Central Atlantic" "Eastern Central Atlantic" "Eastern Central Pacific" "Eastern Central Pacific" ...
##  $ Code                                                                   : chr [1:51] NA NA NA NA ...
##  $ Year                                                                   : num [1:51] 2015 2017 2015 2017 2015 ...
##  $ Share of fish stocks within biologically sustainable levels (FAO, 2020): num [1:51] 57.1 57.1 86.7 86.7 73.1 ...
##  $ Share of fish stocks that are overexploited                            : num [1:51] 42.9 42.9 13.3 13.3 26.9 ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   Entity = col_character(),
##   ..   Code = col_character(),
##   ..   Year = col_double(),
##   ..   `Share of fish stocks within biologically sustainable levels (FAO, 2020)` = col_double(),
##   ..   `Share of fish stocks that are overexploited` = col_double()
##   .. )
##  - attr(*, "problems")=<externalptr>

‘stock’ has 5 variables and 51 observations. The ‘Entityy’ and ‘Code’ variable are character while others are numerical.

2.3.2 ‘stock’ - Data quality Check

Similar to ‘consumption’, all missing value are from ‘Code’.

2.4 seafood-and-fish-production-thousand-tonnes.csv

2.4.1 ‘production’ - Data Dictionary

Entity: Country/entity Code: Country code Year: Year
Pelagic Fish - 2763 - Production - 5510 - tonnes: Pelagic Fish Crustaceans - 2765 - Production - 5510 - tonnes: Crustaceans Cephalopods - 2766 - Production - 5510 - tonnes: Cephalopods Demersal Fish - 2762 - Production - 5510 - tonnes: Demersal Freshwater Fish - 2761 - Production - 5510 - tonnes: Freshwater Molluscs, Other - 2767 - Production - 5510 - tonnes: Molluscs Marine Fish, Other - 2764 - Production - 5510 - tonnes: Marine

## spec_tbl_df [10,326 × 10] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ Entity                                                                                                             : chr [1:10326] "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
##  $ Code                                                                                                               : chr [1:10326] "AFG" "AFG" "AFG" "AFG" ...
##  $ Year                                                                                                               : num [1:10326] 1961 1962 1963 1964 1965 ...
##  $ Commodity Balances - Livestock and Fish Primary Equivalent - Pelagic Fish - 2763 - Production - 5510 - tonnes      : num [1:10326] NA NA NA NA NA NA NA NA NA NA ...
##  $ Commodity Balances - Livestock and Fish Primary Equivalent - Crustaceans - 2765 - Production - 5510 - tonnes       : num [1:10326] NA NA NA NA NA NA NA NA NA NA ...
##  $ Commodity Balances - Livestock and Fish Primary Equivalent - Cephalopods - 2766 - Production - 5510 - tonnes       : num [1:10326] NA NA NA NA NA NA NA NA NA NA ...
##  $ Commodity Balances - Livestock and Fish Primary Equivalent - Demersal Fish - 2762 - Production - 5510 - tonnes     : num [1:10326] NA NA NA NA NA NA NA NA NA NA ...
##  $ Commodity Balances - Livestock and Fish Primary Equivalent - Freshwater Fish - 2761 - Production - 5510 - tonnes   : num [1:10326] 300 300 300 300 300 300 400 400 460 460 ...
##  $ Commodity Balances - Livestock and Fish Primary Equivalent - Molluscs, Other - 2767 - Production - 5510 - tonnes   : num [1:10326] NA NA NA NA NA NA NA NA NA NA ...
##  $ Commodity Balances - Livestock and Fish Primary Equivalent - Marine Fish, Other - 2764 - Production - 5510 - tonnes: num [1:10326] NA NA NA NA NA NA NA NA NA NA ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   Entity = col_character(),
##   ..   Code = col_character(),
##   ..   Year = col_double(),
##   ..   `Commodity Balances - Livestock and Fish Primary Equivalent - Pelagic Fish - 2763 - Production - 5510 - tonnes` = col_double(),
##   ..   `Commodity Balances - Livestock and Fish Primary Equivalent - Crustaceans - 2765 - Production - 5510 - tonnes` = col_double(),
##   ..   `Commodity Balances - Livestock and Fish Primary Equivalent - Cephalopods - 2766 - Production - 5510 - tonnes` = col_double(),
##   ..   `Commodity Balances - Livestock and Fish Primary Equivalent - Demersal Fish - 2762 - Production - 5510 - tonnes` = col_double(),
##   ..   `Commodity Balances - Livestock and Fish Primary Equivalent - Freshwater Fish - 2761 - Production - 5510 - tonnes` = col_double(),
##   ..   `Commodity Balances - Livestock and Fish Primary Equivalent - Molluscs, Other - 2767 - Production - 5510 - tonnes` = col_double(),
##   ..   `Commodity Balances - Livestock and Fish Primary Equivalent - Marine Fish, Other - 2764 - Production - 5510 - tonnes` = col_double()
##   .. )
##  - attr(*, "problems")=<externalptr>

There are 10 variables and 10,326 observations. The ‘Entityy’ and ‘Code’ variable are character while others are numerical.

2.4.2 ‘production’ - Data quality Check

3 Expected findings

** What have been the performances of the top 10 seafood producers? **

** What has been the trend of fish stocks in the world? **

4 Analysis and findings

4.1 What have been the performances of the top 10 seafood producers?

4.1.1 Method:

We are going to specify the analysis area by focusing on the top 10 seafood producers and digging the data from a wider prospective.

Here we use ‘captured_vs_farmed’ data set. The first reason is that ‘captured_vs_farmed’ contains less NA value than ‘production’. Secondly, we tend to focus on the trend of ‘captured vs farmed’ seafood production for those top producers.

In addition, the use of sorted data from the original report help us quite a lot to simplify the process. These data includes: ‘country_cf’, ‘dat’, ‘world_cap_farm’ and ‘captured_vs_farmed_long’.

To answer this question, we took the following steps:

1 We worked out the overall top ten producers between 2015-2018 as well as the top 10 in terms of captured or farmed fish during the same period. We took the average production for each country 2015 - 2018 because there are many missing value in 2018. This allows us to present a accurate picture to reflect the most recent seafood productivity of each country.

2 Next we figured out the top ten producers of 1960s using data between 1960-1970 due to the problem of missing value.Then we compared the top 10 of 1960s with the list of 2010s.

3 We would take a look at the percentage contribution for each of the top producers.

4 Lastly, we tried to find out what were the changes of production between 1960s and 2010s.

4.2 Findings:

From the 3 plots above, we can see that China and Indonesia are the top 2 producers in each of the three ranks. China takes the dominate position of seafood production.

In addition, the U.s and Peru are the overall top producer but did not shown on the rank of aquaculture fishing. Similarly, Philippines and Bangladesh are not listed in the top captured seafood producer. To sum up, only six countries present a strong productivity in both captured and farmed production.

Compared the top 10 producer in 1960s with that of 2010s, we can see that three developed countries:South Africa, Canada and Spain were the top 10 in 1960s but did not shown on the list of 2010s.On the other hand, three developing countries: Vietnam, Philippines and Bangladesh are the new countries listed in 2010s rank. Geographically, all the new comers in 2010s are located in South-east Asia.

Moreover, considering what we observed in Graph.5a-5c, it seems like the high aquaculture production is the main reason for Vietnam, Philippines and Bangladesh to be ranked as top 10 producer in 2010s.

It can be seen from Graph.5h that both China and Indonesia showed a more than 20 times increase in seafood production from 1960-2018. Following this are the tier 2 countries: Vietnam, India, Bangladesh and Philippines, a huge increase between 5 to 10 times. The U.s and Norway showed a 110% and 59% rise respectively.

On the other hand, Peru and Japan experienced a considerable decline in seafood production, which were 37% and 41%. However, they are still the top 10 producer all over the world.

Here we tend to further explore what happened to Japan and Peru. As we can see from Graph.5i, the farmed production of these two countries remains at the same level from 1960-2018, while the captured production declined quite a lot compare to the peak.

Moreover, compared Graph.5i and 5h, it is clearly that the countries with relatively lower increase (The U.s, Norway) or decline (Peru, Japan) in the total production, all presenting a poor development in farmed production. In other words, those four countries produce more captured fish than farmed seafood.

In comparison, other six countries with relatively higher overall increase, experiencing a significant upward trend in farmed production. The level of aquaculture fishing of these six countries are higher than captured fish.

4.2.1 Summary and interesting findings:

1 Developing countries take more position in top ten seafood producers.

2 ALL the new comers on the top 10 list in 2010s are from South-east Asia.

3 The center of world’s seafood production is now in West pacific and Southeast Pacific.

4 Farmed production contribute more than captured seafood to the improvement of countries’ overall productivity.

5 China and Indonesia are the top 2 seafood producers in total production as well as the farmed and captured ranking. Over the last 60 years, these two countries experienced the most significant upward trend in seafood production.

6 Peru and Japan were the top 2 countries in 1960s. However, they were the only two countries which showed a decrease in seafood production within the top 10 list over the last 60 years.

##What has been the trend of fish stocks in the world?

4.2.2 Method:

Here we use score ‘stock’ and ‘consumption’ data. We not only analysed data within ‘stock’, but also compare ‘stock’ with ‘consumption’.

Similarly, we used some cleaned data from original report including ‘consumption’ and ‘annual_consumption’.

1 We firstly plotted a graph to have a comparison of sustainable vs overexploited based on trend and proportion.

2 Previous analysis reveals the world’s trend based on the stock type. Here we would like to go further by analyzing the trend based on each region.

3 Also, we are going to compare the world’s trend of overexploited stock with the trend of world seafood consumption. A considerable decline in seafood consumption around 1989 was observed in previous analysis. By comparing these two data sets, we would like to see if any similar pattern exist.

It is obviously that sustainable stock is always having a large proportion then those overexploited.

Although the level of overexploited stock increasing with fluctuation overtime, it has never exceeded 40% of the total.

Here we compared the proportion of overexploited stock with the world seafood consumption.

We plotted 8 vertical lines. Each of them represents a start / end point of a particular up / downward trend. Also, vertical lines with the same color corresponds to each other in different graphs. Since a major decline in consumption in 1989 was observed in previous analysis, we set up 1989 as the first spot.

It can be seen from Graph.6a and 6b that the trend of overexploited stock and world seafood consumption have a similar pattern. For each individual trend between two vertical lines in Graph.6a, we can find out a similar trend between two vertical lines with the corresponded color in Graph.6b.

It is interesting that most of the vertical line (except the trend between yellow and purple) in the Graph.6b are on th left to the line with same color in Graph.6c. This implies that the trend in consumption always delayed a bit compared with the corresponding trend in overexploited stock.

Since the level of sustainable stock shows a opposite trend to the overexploited stock, we can also conclude that seafood consumption has a reverse movement with sustainable stock since 1989.

Next, we plotted graphs to check the stock level based on regions.

As we can see from Graph.6f,the world’s sustainable stock declined 1.2% between 2015-2017. The main factor contributed to this drop is the decreased sustainable stock in Northwest Pacific and Northwest Central Atlantic which were 21% and 24% respectively.

In comparison, Southeast Pacific, Southwest Pacific and Southwest Atlantic experienced the largest increase in sustainable stock.

It is worth noting that the stock level of Southeast Central Atlantic,Eastern Central Pacific and Eastern Central Atlantic were almost unchanged.

4.2.3 Summary and interesting findings:

1 The level of overexploited stock increasing with fluctuation overtime.

2 There are similar pattern between the trend of overexploited stock and world seafood consumption since 1989.

3 Compared with stock level, there are lags shown in the world consumption for each similar trend.

4 Some regions indicate very stable stock level between 2015-17.

5 Limitation and recommendation

1 With missing value and incomplete data in ‘production’ and ‘captured vs farmed’, this study may lack accuracy for visualization.

2 Only 2 years data is available for most entities in ‘stock’ data set, which make it difficult to analyzing long term performance.

3 There is not a clear definition about those ‘Entity’ in ‘stock’ data. In this case, we are unable to link those entities with countries to have a deeper analysis.

4 Future research would benefit from the use of more datasets to broaden the prospective of analysis. For example, a data of stock level based on each country’s performance may help us to dig more insights in terms of sustainable fishing.

6 References